62 строки
2.0 KiB
Python
62 строки
2.0 KiB
Python
'''MobileNet in PyTorch.
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See the paper "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications"
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for more details.
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class Block(nn.Module):
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'''Depthwise conv + Pointwise conv'''
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def __init__(self, in_planes, out_planes, stride=1):
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super(Block, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=in_planes, bias=False)
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self.bn1 = nn.BatchNorm2d(in_planes)
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self.conv2 = nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=1, padding=0, bias=False)
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self.bn2 = nn.BatchNorm2d(out_planes)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = F.relu(self.bn2(self.conv2(out)))
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return out
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class MobileNet(nn.Module):
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# (128,2) means conv planes=128, conv stride=2, by default conv stride=1
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cfg = [64, (128,2), 128, (256,2), 256, (512,2), 512, 512, 512, 512, 512, (1024,2), 1024]
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def __init__(self, num_classes=10):
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super(MobileNet, self).__init__()
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self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(32)
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self.layers = self._make_layers(in_planes=32)
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self.linear = nn.Linear(1024, num_classes)
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def _make_layers(self, in_planes):
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layers = []
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for x in self.cfg:
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out_planes = x if isinstance(x, int) else x[0]
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stride = 1 if isinstance(x, int) else x[1]
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layers.append(Block(in_planes, out_planes, stride))
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in_planes = out_planes
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return nn.Sequential(*layers)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.layers(out)
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out = F.avg_pool2d(out, 2)
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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def test():
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net = MobileNet()
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x = torch.randn(1,3,32,32)
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y = net(x)
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print(y.size())
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# test()
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